Factors affecting Family Physician follow-up 30 days post-discharge from a Canadian Academic Emergency Department
Bibliographic record
Abstract
Close outpatient follow-up of patients discharged from the emergency department (ED) has been associated with improved antimicrobial stewardship, medication compliance, and decreased mortality. Despite these clear benefits, studies have shown most patients do not receive follow-up from specialists or Family Physicians (FP). While age, race and insurance status may be factors in Australia and the United States, there remains a paucity of Canadian studies investigating potential factors that influence follow-up. This retrospective cohort study aimed to elucidate factors associated with Family Physician follow up within 30 days at two urban, academic Family Medicine clinics. Our study included patients aged 18 or older who have an academic Family Physician and visited a London Health Sciences Centre ED between January 1, 2021 and June 1, 2021. A binary logistic regression was used to determine if a specific patient or provider factor was associated with follow-up. Of the 367 cases that met criteria, 220 (60%) patients received Family Physician follow-up within 30 days. Additionally, 51 patients (23%) received specialist follow-up within 30 days. A higher number of medications (OR 1.12 p=0.003) and a Family Physician appointment within the 90 days preceding the ED visit (OR 2.51, p<0.001) were significantly predictive of Family Physician follow-up. The use of a Family Physician referral form, documented discharge instructions, and increasing comorbidity (as documented by the Charlson Comorbidity Index) were not associated with a higher odds of follow-up. These data suggest that patients on numerous medications may require close follow-up for monitoring, dose adjustments, and reassessment. Additionally, those patients with recent Family Physician visits may have stronger relationships with their provider, increasing their likelihood of follow-up. Based on this study, there is insufficient evidence to suggest that documented discharge instructions nor the use of a FP referral form impact the rate of follow-up. Future work should focus on an optimal mechanism to ensure Family Physician follow-up, when required, in urban centres. The impact of mental health and substance use disorders on the rate of follow-up should also be evaluated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".